INTRUSION DETECTION USING HIDDEN NAVIE BAYES CLASSIFIER WITH FEATURE REDUCTION

Abstract—INTRUSION DETECTION USING HIDDEN NAVIE BAYES CLASSIFIER WITH FEATURE REDUCTION. Intrusion detection system (IDS) is the system which identifies malicious activity on the network. As the Internet volume is increasing rapidly, security against the real time attacks and their fast detection issues gain attention of many researchers. Data mining methods can be effectively applied to (IDS) to tackle the problems of dynamic huge network data and to improve IDS performance. We can reduce the time complexity by selecting only useful features to build model for classification. There are many features selection techniques are developed either to select the features or extract features. In this paper, an evolutionary approach for feature selection is proposed which is based on mathematical intersection principle. Genetic algorithm (GA) is used as a search method while selecting features from full NSL KDD data set along with the intersection principle of selecting those only who appears everywhere in the experiment. The results of proposed approach when compared using classifiers, < Final Year Projects > it shows tremendous growth in accuracy of a Naïve Bayes classifier with reduced time and minimum number of features.